Re: st: random subsample - sample weights

Ellen and Stas--
The Fairlie decomposition alluded to is implemented without weights in
the SSC package -fairlie- (references are in the help file), and
depends not only the specifics of the match but on the order of
variables used to match!
http://ssrn.com/abstract=497302http://ftp.iza.org/dp1917.pdf
Fairlie uses the delta method to approximate std errors, but it's not
clear that its use is justified in this case of one-to-one matching.
In any case, you may want to take Ben Jann's -fairlie- command as a
starting point, or you may prefer to investigate the alternative due
to Yun:
http://ftp.iza.org/dp877.pdf
and many other related papers available via a web search.
On 2/15/07, Stas Kolenikov <skolenik@gmail.com> wrote:

There is a bunch of different implementations of probability
proportional to size (PPS) sampling floating around

On 2/15/07, Ellen Van de Poel <vandepoel@few.eur.nl> wrote:
> I want to draw a random subsample from my data, but taking into account my
> sample weights.
> I thought of inflating my data (with the command "expand") to get rid of the
> weights and then draw a random subsample from the expanded data. But I'm not
> sure whether this is correct, since then I can have the same observation
> multiple times in the random subsample?
> Thereafter I match this random subsample with another sample (I'm doing a
> Fairlie decomposition). So I guess it is necessary to account for the sample
> weights when I match the random subsample with another sample?